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Biometrics has become a viable and popular solution for applications which require secure authentication. In spite of the advantages of biometrics as an automatic authentication technology, the usage of biometric characteristics raises significant concerns regarding personal data and privacy of subjects in these systems. To address these concerns, several biometric template protection schemes have been proposed in the literature to achieve trustworthy, reliable and privacy-preserving systems. In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) hashing, Bloom filters, and two schemes based on Index-of-Maximum (IoM) hashing (i.e. IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we introduce a CB scheme (as a baseline) based on user-specific random transformations followed by binarization. We evaluate the unlinkability, irreversibility, and recognition performance (which are the required criteria by the ISO/IEC 24745 standard) of these CB schemes on deep learning-based templates extracted from different physiological and behavioural biometric characteristics including face, voice, finger vein, and iris. Our experiments show that all the studied CB schemes are almost unlinkable for different characteristics. We also observe that the mutual information (MI) between protected and unprotected templates varies according to the scenario and biometric characteristic. In terms of recognition accuracy, our study shows that deep templates protected by Bloom filters suffer from a drop in performance, while other CB schemes achieve competitive accuracies for different biometric characteristics. We provide an open-source implementation of all the experiments presented to facilitate the reproducibility of our results: https://github.com/otroshi/benchmark_cb .
Specific ion effects are widespread and play a crucial role in many fields across chemical and biological systems. Here, the identity of the constituent ions is a determining factor, not just their charge or concentration. Therefore, these effects show a complex dependence on properties such as ion polarizability, size, and hydration shell. The formation of non-close-packed structures (NCPS) of colloidal particles on gold substrates in the presence of different ions enables the investigation of ion effects by examining particle arrangement. Due to their chemical similarity and common valence, lanthanide (Ln3+) ions were explored to determine the effect of polarizability exclusively. It is shown that the tendency of particles to aggregate correlates with the polarizability of the Ln3+ ions. Colloidal stability was increased in the presence of highly polarizable ions (La3+), whereas a less polarizable ions (Lu3+) led to the promotion of aggregation. Contrary, the nearest neighbor distances of the colloidal particles remain approximately constant through all lanthanide ions. Moreover, it could be shown that in the presence of Ln3+ ions, the maximum surface coverage of particles is consistently lower than for monovalent ions or in the random sequential adsorption (RSA) model because multivalent co-ions induce long–range interactions. Therefore, using trivalent co-ions allows for adjustment of interparticle distances over a larger range than monovalent ions. The maximum surface coverage of particles at ionic strengths from 0.1 to 1 mM decreases with increasing ion valency. A maximum surface coverage of 0.43 is achieved with GdCl3 at an ionic strength of 300 mM.
Disinformation, misinformation, and harmful online content have increasingly shaped public discourse, political decision-making, and societal trust. Through the expanding role of social networks and digital news platforms, users are confronted with large volumes of heterogeneous information, making it difficult to distinguish reliable content from deceptive or manipulative material. Existing machine learning approaches often focus on binary classification without addressing the linguistic, narrative, and contextual structures that contribute to misleading content. In addition, related forms of harmful communication - such as hate speech, toxic language, propaganda, biased reporting, and extremist narratives - are closely intertwined with disinformation, yet are rarely analyzed together within a unified research setting. To address these challenges, this dissertation presents DisDETECT, a system for the semi-automatic detection, analysis, and contextualization of disinformation in German-language online media. Two datasets were created for this purpose: one covering short texts and one consisting of long-form articles, both including disinformation, narrative structures, propaganda cues, hate and toxic language, and other stylistic or communicative phenomena. Based on these resources, an information extraction pipeline identifies entities, events, topics, claims, narratives, framing techniques, and indicators of bias at both document and corpus level. The extracted elements are integrated into a semantically enriched knowledge graph that organizes structured triples together with embedding-based representations for subsequent analytical steps and knowledge infusion. The methodological component of the thesis introduces a hybrid classification pipeline that combines transformer-based language models, linguistic features, and knowledge graph embeddings. Nineteen models were developed and evaluated across binary, multiclass, and multilabel tasks relevant to disinformation and harmful communication. Transfer learning was applied using multiple external datasets for pre-training and fine-tuning. The outputs of these models form the basis of an assessment mechanism that assigns degrees of reliability to textual content. To support interpretability, a rule-based explanation layer and large language model components generate textual explanations of model decisions, which are presented within the interactive visual analytics interface of the DisDETECT system. The results of this work show that the combination of information extraction, transfer learning, knowledge infusion, and hybrid classification methods supports detailed analysis of disinformation and related communication styles. A user evaluation of the DisDETECT system provides insights into how experts interact with the interface and interpret the presented results. By integrating linguistic, narrative, and contextual perspectives into a single analytical environment, this dissertation contributes to a broader understanding of how harmful content emerges, overlaps, and propagates across online media.
Legacy systems are business-critical software products that are difficult to understand and modify. Many are written in dynamically typed languages like JavaScript or Python, which lack the benefits of static type annotations for documentation, error prevention, and maintainability. Manually retrofitting types to large legacy codebases requires significant effort, often taking years. Existing automated approaches have limited practical applicability: static type inference cannot handle dynamic language features, dynamic inference requires comprehensive test suites, and probabilistic inference is inherently unsound. No prior approach achieves both high type coverage and correctness at the repository level.
In this thesis, I investigate the use of agentic Artificial Intelligence (AI)—systems composed of Large Language Model (LLM)-based agents—to enable automated typing of legacy software projects. Through a systematic literature review, I identify the challenges and limitations of existing approaches. To overcome these, I propose a three-phase typing approach: (1) type-checking setup with initial error resolution, (2) type annotation and definition generation, and (3) strict type checking with refactoring. I implement the first two phases in AgenticTyper, an open-source tool for JavaScript and TypeScript repositories. My key innovation is a transpilation-based behavior preservation mechanism that prevents unintended modifications and addresses trust issues when using LLMs on production code.
I evaluate the approach through a case study on three proprietary legacy repositories with 456,000 lines of code. Phase one successfully sets up type checking and resolves all 633 initial errors for $25 in LLM API costs, compared to eight hours of manual effort for a single repository. Phase two achieves 86-96% type coverage with zero remaining type errors, costing $4 to $95 and requiring 4-70 hours of human review time depending on repository size. AgenticTyper surfaces 135 manually confirmed bugs and 40 possible bugs through increased type coverage that were previously unknown to the developers. A comparison with prior work shows that most existing tools are inapplicable or achieve at most 81.5% coverage while introducing thousands of type errors. Furthermore, a non-agentic LLM baseline fails to preserve runtime behavior.
The results validate that agentic AI enables automated typing at a practical level that prior approaches could not achieve, substantially reduces manual effort compared to fully manual typing, and identifies numerous existing issues. However, full automation is not achieved: human review remains essential for high-quality results, as manual cleanup reduces the number of added lines by 15-41% without sacrificing type coverage. Ultimately, the resulting pull requests, comprising 35,000 inserted lines, were merged by the development team, demonstrating the approach's practical value.
Histone deacetylase 8 (HDAC8) is a key enzyme involved in regulating gene expression and tumor development, positioning it as an attractive target for neuroblastoma. In this work, we designed and synthesized a novel series of substituted [1,2,4]Triazolo[4,3-a]quinoline derivatives to investigate their potential as HDAC8 inhibitors. Structural insights into their inhibitory activity were gained through molecular docking studies, highlighting critical interactions within the HDAC8 active site. To assess the stability of these interactions, molecular dynamics simulations were performed, confirming that the compounds maintained strong and stable binding within the HDAC8 enzyme. The most promising inhibitors 9h and 9m demonstrated significant efficacy in IMR-32 neuroblastoma cells, but had much weaker effects on the HCT116 and MCF7 cancer cell lines, as well as on the normal control cell line, HEK293. Further biological evaluations, including colony formation and cell migration assays, revealed their potential to inhibit the growth and spread of neuroblastoma tumor cells. Additional studies on cell cycle progression, apoptosis induction, and SMC3 acetylation indicated increased acetylation levels without altering total SMC3 protein levels, suggesting effective HDAC8 target engagement. In summary, comprehensive molecular modelling and biological assessments have demonstrated the strong potential of these non-hydroxamate-based HDAC8 inhibitors for treating neuroblastoma.
NaMnF3:Yb3+, Er3+ upconversion nanoparticles (UCNPs) are highly attractive for multimodal biomedical imaging because they combine a single red emission band with magnetic properties, allowing their use as T1 contrast agents in magnetic resonance imaging. However, compared to other magnetic or red-emitting UCNPs, their synthesis remains challenging, so they are rarely used despite their great potential. Syntheses based on hydrothermal approaches usually yield rather large or aggregated and
often polydisperse and polymorphic particles. Reported thermal decomposition
methods, in principle more suited to yield highly crystalline, small, and monodisperse UCNPs, are questionable because of the unclear phase and doping state of the NaMnF3 particles despite their strong red emission. This is due to the parallel formation of red
luminescent NaYbF4 particles, which has not been sufficiently investigated. In this work, different thermal decomposition routes are studied, and the particles are analyzed to optimize them for high dopant integration and strong red luminescence at 655 nm. An optimized synthesis route for well-defined phase-pure particles in a size range
from 20 to 25 nm was developed. This approach is based on separately
preparing Mn2+, Er3+, and Yb3+ oleates and then introducing them into the reaction, followed by a purification step. This leads to phase-pure UCNPs and substantial increases in dopant concentrations as well as luminescence compared to reported
synthesis routes. After their hydrophilic functionalization with polyacrylic acid, the ability of the particles to serve as magnetic resonance imaging (MRI) contrast agents with a high Mn2+ content was demonstrated by NMR relaxometry.
In everyday outpatient care, office-based medical professionals often face a growing volume of patient records, spanning years or even decades, while consultation times remain critically short. Much of this information is embedded in unstructured documents such as physician letters, lab reports, and diagnostic notes. The lack of time makes it difficult for physicians to comprehend the patients’ medical history and to base decisions on the full health and disease history. This paper presents a modular Visual Analytics approach particularly designed to support general practitioners in navigating long-term patient histories. Using transformer-based large language models, the system automatically extracts and classifies relevant clinical entities from unstructured documents. These extracted elements are mapped to a set of predefined data categories and visualized through tailored, interactive components, such as medication intake timelines, blood count diagrams, and named-entity highlighted texts. Visual components are selected dynamically based on content type and clinical relevance, allowing medical professionals to explore patient histories efficiently and to identify critical information at a glance. The modular architecture facilitates integrating new document types and visual modules, ensuring flexibility and scalability. Designed for real-world applicability, the system aims to reduce cognitive load, improve information accessibility, and support time-sensitive decision-making in primary care.
Featured Application
The ML Ontology presented in this article is the information backbone of a data science platform used for training machine learning models from datasets. Features include a training configuration wizard and interactive help.
This article presents an industry-ready ontology for the machine learning domain, which is named “ML Ontology”. While based on lightweight modelling languages, ML ontology provides novel features including built-in queries and quality assurance, as well as sophisticated reasoning. With ca. 700 individuals that define key ML concepts and ca. 5000 RDF triples, ML Ontology ranks among the largest domain-specific ontologies for ML. An experiment to estimate the correctness and completeness of ML terminology included in ML Ontology indicates an F1-score of 0.83. A benchmark evaluating query performance reveals query response times far below 100 ms even for complex queries and memory consumption below 3.5 MB. Its industry-readiness is demonstrated by benchmarks as well as two use case implementations within a data science platform. ML Ontology is open source and published under an MIT license.
Neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD) are characterized by complex pathologies with progressive neurodegeneration, protein misfolding, oxidative stress, and persistent inflammation. Recent findings indicate the pivotal involvement of epigenetic disruption, particularly aberrant histone deacetylase (HDAC) activity, in disease initiation and progression. In the current review, we systematically discuss the mechanistic function of HDACs across all classes (I, IIa, IIb, III, and IV) in neurodegenerative disease mechanisms, such as their involvement in the modulation of gene expression, mitochondrial function, proteostasis, and neuronal survival. We discuss the therapeutic potential, as well as limitations, of HDAC inhibitors (HDACis), such as pan-inhibitors and isoenzyme-selective inhibitors, and new multi-target-directed ligands with HDAC inhibition combined with acetylcholinesterase modulation, PDE modulation, MAO-B inhibition, or NMDAR modulation. Particular emphasis is placed on the development of HDAC6-selective inhibitors with enhanced brain permeability and reduced toxicity, which have shown promising preclinical efficacy in ameliorating hallmark pathologies of AD, PD, and HD. In addition, s-triazine-based scaffolds have recently emerged as promising chemotypes in HDAC inhibitor design, offering favorable pharmacokinetic profiles, metabolic stability, and the potential for dual-target modulation relevant to neurodegeneration. The review also explores the future of HDAC-targeted therapies, including PROTAC degraders, dual-inhibitor scaffolds, and sustainable, BBB-penetrant molecules. Collectively, this review underscores the importance of HDAC modulation as a multifaceted strategy in the treatment of neurodegenerative diseases and highlights the need for continued innovation in epigenetic drug design.
This paper examines the relationship between wealth taxation and Sustainable Development Goals (SDGs) in OECD member countries and analyzes the impacts of wealth (related) taxes on social, environmental, and economic goals for the period of 2000–2021. The results from panel data estimations utilizing fixed effects and Driscoll‐Kraay standard errors indicate that wealth taxes positively contribute to social goals (such as reducing inequalities) but negatively affect economic goals. In the data, we observe that wealth taxes are as successful as corporate income taxes in generating the necessary finance for governments. This underscores that wealth taxes hold significant revenue potential in tackling real‐world problems such as climate change. However, our empirical results do not show any meaningful impact of wealth taxes on environmental objectives. With their current forms, wealth taxes lead to opposing effects on SDGs in OECD countries. Developing effective implementation strategies for wealth taxes is, therefore, essential for promoting future economic activities toward environmental sustainability and well‐being.